class_docstr: Side-Aware Boundary Localization (SABL) for RoI-Head.
  
  Side-Aware features are extracted by conv layers
  with an attention mechanism.
  Boundary Localization with Bucketing and Bucketing Guided Rescoring
  are implemented in BucketingBBoxCoder.
  
  Please refer to https://arxiv.org/abs/1912.04260 for more details.
  
  Args:
      cls_in_channels (int): Input channels of cls RoI feature.             Defaults to 256.
      reg_in_channels (int): Input channels of reg RoI feature.             Defaults to 256.
      roi_feat_size (int): Size of RoI features. Defaults to 7.
      reg_feat_up_ratio (int): Upsample ratio of reg features.             Defaults to 2.
      reg_pre_kernel (int): Kernel of 2D conv layers before             attention pooling. Defaults to 3.
      reg_post_kernel (int): Kernel of 1D conv layers after             attention pooling. Defaults to 3.
      reg_pre_num (int): Number of pre convs. Defaults to 2.
      reg_post_num (int): Number of post convs. Defaults to 1.
      num_classes (int): Number of classes in dataset. Defaults to 80.
      cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
      reg_offset_out_channels (int): Hidden and output channel             of reg offset branch. Defaults to 256.
      reg_cls_out_channels (int): Hidden and output channel             of reg cls branch. Defaults to 256.
      num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
      num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
      reg_class_agnostic (bool): Class agnostic regresion or not.             Defaults to True.
      norm_cfg (dict): Config of norm layers. Defaults to None.
      bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
      loss_cls (dict): Config of classification loss.
      loss_bbox_cls (dict): Config of classification loss for bbox branch.
      loss_bbox_reg (dict): Config of regression loss for bbox branch.
code: def get_bboxes(
      self,
      rois,
      cls_score,
      bbox_pred,
      img_shape,
      scale_factor,
      rescale=False,
      cfg=None,
  ):
      if isinstance(cls_score, list):
          cls_score = sum(cls_score) / float(len(cls_score))
      scores = (
          F.softmax(cls_score, dim=1) if cls_score is not None else None
      )
  
      if bbox_pred is not None:
          bboxes, confids = self.bbox_coder.decode(
              rois[:, 1:], bbox_pred, img_shape
          )
      else:
          bboxes = rois[:, 1:].clone()
          confids = None
          if img_shape is not None:
              bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
              bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
  
      if rescale and bboxes.size(0) > 0:
          if isinstance(scale_factor, float):
              bboxes /= scale_factor
          else:
              bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
  
      if cfg is None:
          return bboxes, scores
      else:
          det_bboxes, det_labels = multiclass_nms(
              bboxes,
              scores,
              cfg.score_thr,
              cfg.nms,
              cfg.max_per_img,
              score_factors=confids,
          )
  
          return det_bboxes, det_labels
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
label_desc: Use image node_id 16 for calling the SABLHead obj's underlying member method code with example usage: obj.get_bboxes(rois, cls_score, bbox_pred, img_shape, scale_factor, rescale, cfg) and returns: bboxes,  scores, det_bboxes,  det_labels